Detection of plant diseases with multi-stage, multi-scale deep learning

ABSTRACT

In some embodiments, a computer-implemented method is disclosed. The method comprises obtaining a first digital model for classifying an image into a class of a first set of classes corresponding to a first plurality of plant diseases, a healthy condition, or a combination of a second plurality of plant diseases; obtaining a second digital model for classifying an image into a class of a second set of classes corresponding to the second plurality of plant diseases; receiving a new image from a user device; applying the first digital model to a plurality of first regions within the new image to obtain a plurality of classifications; applying the second digital model to one or more second regions, each corresponding to a combination of multiple first regions of the plurality of first regions, to obtain one or more classifications, the multiple first regions being classified into the class corresponding to the combination of the second plurality of plant diseases; transmitting classification data related to the plurality of classifications into a class corresponding to one of the first plurality of plant diseases or the healthy condition and the one or more classifications to the user device.

BENEFIT CLAIM

This application claims the benefit under 35 U.S.C. § 120 as acontinuation of application Ser. No. 16/662,017, filed Oct. 23, 2019,which claims the benefit under 35 U.S.C. § 119(e) of provisionalapplication 62/750,143, filed Oct. 24, 2018, the entire contents ofwhich are hereby incorporated by reference for all purposes as if fullyset forth herein. Applicant hereby rescinds any disclaimer of claimscope in the parent applications or the prosecution history thereof andadvises the USPTO that the claims in this application may be broaderthan any claim in the parent applications.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyright orrights whatsoever. © 2015-2020 The Climate Corporation.

FIELD OF THE DISCLOSURE

The present disclosure relates to the technical areas of plant diseasedetection and machine learning. The present disclosure also relates tothe technical area of processing images at different scales to recognizediseases having symptoms of different sizes.

BACKGROUND

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

Plant disease detection is important in agriculture. Today an automatedapproach often involves classifying plant photos by learning from samplephotos. Each photo can show a leaf having disease symptoms. Sometimes,these symptoms are caused by multiple diseases. Sometimes, thesesymptoms have different sizes or overlap one another. It would behelpful to have an efficient and accurate approach of recognizing theplant diseases infecting the leaf from such a photo without requiring assamples a large number of photos showing various symptoms of these plantdiseases.

SUMMARY

The appended claims may serve as a summary of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using agronomic data provided by one or more datasources.

FIG. 4 is a block diagram that illustrates a computer system upon whichan embodiment of the invention may be implemented.

FIG. 5 depicts an example embodiment of a timeline view for data entry.

FIG. 6 depicts an example embodiment of a spreadsheet view for dataentry.

FIG. 7A illustrates an example approach of extracting sample images froma photo showing symptoms of a plant disease that are relatively small.

FIG. 7B illustrates an example approach of extracting sample images froma photo showing symptoms of a plant disease that are relatively large.

FIG. 8 illustrates an example process of recognizing plant diseaseshaving multi-sized symptoms from a plant image using multiple digitalmodels.

FIG. 9A illustrates an example prediction map showing results ofapplying a first digital model to a plant image to recognize plantdiseases having relatively small symptoms.

FIG. 9B illustrates an example prediction map showing results ofapplying a second digital model to a plant image to recognize plantdiseases having relatively large symptoms.

FIG. 10 illustrates an example method performed by a server computerthat is programmed for recognizing plant diseases having multi-sizedsymptoms from a plant image.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present disclosure. It will be apparent, however,that embodiments may be practiced without these specific details. Inother instances, well-known structures and devices are shown in blockdiagram form in order to avoid unnecessarily obscuring the presentdisclosure. Embodiments are disclosed in sections according to thefollowing outline:

1. GENERAL OVERVIEW

2. EXAMPLE AGRICULTURAL INTELLIGENCE COMPUTER SYSTEM

-   -   2.1. STRUCTURAL OVERVIEW    -   2.2. APPLICATION PROGRAM OVERVIEW    -   2.3. DATA INGEST TO THE COMPUTER SYSTEM    -   2.4. PROCESS OVERVIEW—AGRONOMIC MODEL TRAINING    -   2.5. IMPLEMENTATION EXAMPLE—HARDWARE OVERVIEW

3. FUNCTIONAL DESCRIPTIONS

-   -   3.1 TRAINING SET AND DIGITAL MODEL CONSTRUCTION    -   3.2 DIGITAL MODEL EXECUTION    -   3.3 EXAMPLE PROCESSES

4. EXTENSIONS AND ALTERNATIVES

1. General Overview

A system for recognizing plant diseases producing multi-sized symptomsfrom a plant photo is disclosed. In some embodiments, the system isprogrammed to build from multiple training sets multiple digital models,each for recognizing plant diseases having symptoms of similar sizes.Each digital model can be implemented with a deep learning architecture,such as a convolutional neural network (CNN), that classifies an imageinto one of several classes. For each training set, the system is thusprogrammed to collect images showing symptoms of one or more plantdiseases having similar sizes. These images are then assigned tomultiple disease classes. For a first one of the training sets used tobuild the first digital model, the system is programmed to also includeimages that correspond to a healthy condition and images of symptomshaving other sizes. These images are then assigned to a no-disease classand a catch-all class. Given a new image from a user device, the systemis programmed to then first apply the first digital model. For at leastthe portions of the new image that are classified by the first digitalmodel into the catch-all class, the system is programmed to then applyanother one of the digital models. The system is programmed to finallytransmit classification data to the user device indicating how eachportion of the new image is classified into a class corresponding to aplant disease or no plant disease at all.

In some embodiments, the plant is corn. Each image can be a digitalimage and is typically a photo showing a corn leaf infected with one ormore diseases. The system can be programmed to build two digital models,a first one for recognizing those corn diseases producing relativelysmall symptoms, and a second one for recognizing those corn diseasesproducing relatively large symptoms.

In some embodiments, for the first training set for building the firstdigital model, the system can be configured to include photos showingmainly symptoms of those diseases having relatively small symptoms.These photos would thus have relatively small sizes. Alternatively, thesystem can be configured to include scaled versions of these photoscorresponding to similar field of views as the originals but having afixed size. The system can be configured to also include photoscorresponding to similar field of views but showing no symptoms orshowing symptoms of those diseases having relatively large symptoms.Therefore, the first digital model is designed to classify a corn imageinto a class corresponding to one of those corn diseases havingrelatively small symptoms or a healthy condition or a catch-all classcorresponding to a combination of those corn diseases having relativelylarge symptoms.

In some embodiments, for the second training set for building the seconddigital model, the system can be configured to include photos showingmainly symptoms of those diseases having relatively large symptoms.These photos would thus have relatively large sizes. Alternatively, thesystem can be configured to include scaled versions of these photoscorresponding to similar field of views as the originals but having afixed size. Therefore, the second digital model is designed to classifya corn image into a class corresponding to one of those corn diseaseshaving relatively large symptoms. The system can be programmed to buildthe first digital model and the second digital model as CNNsrespectively from the first training set and the second training set.

In some embodiments, the system is programmed to receive a new image,such as a new photo of an infected corn leaf, from a user device andapply the digital models to the new image. Specifically, the system isprogrammed to first apply the first digital model to the new image toclassify each first region within the new image into one of the classescorresponding to corn diseases having relatively small symptoms, ahealthy condition, or the combination of corn diseases having relativelylarge symptoms. The system is programmed to next apply the seconddigital model to each second region within the combination of firstregions that have been classified into the catch-all class into one ofthe classes corresponding to corn diseases having relatively largesymptoms. The second region is typically larger than first regioncorresponding to a larger symptom or a larger field of view. The systemcan be programmed to then send classification data related to how eachfirst region or second region is classified into one of the classescorresponding to corn diseases or the healthy condition to the userdevice.

The system produces various technical benefits. The system allowsdetection of multiple plant diseases from one plant image. The systemalso allows detection of one plant disease having relatively smallsymptoms even when such symptoms overlap with relatively large symptomsof another plant disease. In addition, the system also allows detectionof plant diseases having multi-sized symptoms from one plant image. Morespecifically, the system enables association of each of a plurality ofregions within a plant with one of a plurality of plant diseases or ahealthy class, even when the diseases symptoms have different sizes.Furthermore, the multi-stage approach where different digital modelsdesigned to identify separate groups of symptoms are sequentiallyapplied achieves accuracy while requiring relatively few sample imagescompared to the one-stage approach of detect different groups ofsymptoms at once. In particular, the multi-stage approach can utilizemultiple images extracted from an image used to train the one-stageapproach, with the image showing multiple groups of symptoms and eachextracted image showing symptoms of only one of the groups.

Other aspects and features of embodiments will become apparent fromother sections of the disclosure.

2. Example Agricultural Intelligence Computer System

2.1 Structural Overview

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate. In oneembodiment, a user 102 owns, operates or possesses a field managercomputing device 104 in a field location or associated with a fieldlocation such as a field intended for agricultural activities or amanagement location for one or more agricultural fields. The fieldmanager computer device 104 is programmed or configured to provide fielddata 106 to an agricultural intelligence computer system 130 via one ormore networks 109.

Examples of field data 106 include (a) identification data (for example,acreage, field name, field identifiers, geographic identifiers, boundaryidentifiers, crop identifiers, and any other suitable data that may beused to identify farm land, such as a common land unit (CLU), lot andblock number, a parcel number, geographic coordinates and boundaries,Farm Serial Number (FSN), farm number, tract number, field number,section, township, and/or range), (b) harvest data (for example, croptype, crop variety, crop rotation, whether the crop is grownorganically, harvest date, Actual Production History (APH), expectedyield, yield, crop price, crop revenue, grain moisture, tillagepractice, and previous growing season information), (c) soil data (forexample, type, composition, pH, organic matter (OM), cation exchangecapacity (CEC)), (d) planting data (for example, planting date, seed(s)type, relative maturity (RM) of planted seed(s), seed population), (e)fertilizer data (for example, nutrient type (Nitrogen, Phosphorous,Potassium), application type, application date, amount, source, method),(f) chemical application data (for example, pesticide, herbicide,fungicide, other substance or mixture of substances intended for use asa plant regulator, defoliant, or desiccant, application date, amount,source, method), (g) irrigation data (for example, application date,amount, source, method), (h) weather data (for example, precipitation,rainfall rate, predicted rainfall, water runoff rate region,temperature, wind, forecast, pressure, visibility, clouds, heat index,dew point, humidity, snow depth, air quality, sunrise, sunset), (i)imagery data (for example, imagery and light spectrum information froman agricultural apparatus sensor, camera, computer, smartphone, tablet,unmanned aerial vehicle, planes or satellite), (j) scouting observations(photos, videos, free form notes, voice recordings, voicetranscriptions, weather conditions (temperature, precipitation (currentand over time), soil moisture, crop growth stage, wind velocity,relative humidity, dew point, black layer)), and (k) soil, seed, cropphenology, pest and disease reporting, and predictions sources anddatabases.

A data server computer 108 is communicatively coupled to agriculturalintelligence computer system 130 and is programmed or configured to sendexternal data 110 to agricultural intelligence computer system 130 viathe network(s) 109. The external data server computer 108 may be ownedor operated by the same legal person or entity as the agriculturalintelligence computer system 130, or by a different person or entitysuch as a government agency, non-governmental organization (NGO), and/ora private data service provider. Examples of external data includeweather data, imagery data, soil data, or statistical data relating tocrop yields, among others. External data 110 may consist of the sametype of information as field data 106. In some embodiments, the externaldata 110 is provided by an external data server 108 owned by the sameentity that owns and/or operates the agricultural intelligence computersystem 130. For example, the agricultural intelligence computer system130 may include a data server focused exclusively on a type of data thatmight otherwise be obtained from third party sources, such as weatherdata. In some embodiments, an external data server 108 may actually beincorporated within the system 130.

An agricultural apparatus 111 may have one or more remote sensors 112fixed thereon, which sensors are communicatively coupled either directlyor indirectly via agricultural apparatus 111 to the agriculturalintelligence computer system 130 and are programmed or configured tosend sensor data to agricultural intelligence computer system 130.Examples of agricultural apparatus 111 include tractors, combines,harvesters, planters, trucks, fertilizer equipment, aerial vehiclesincluding unmanned aerial vehicles, and any other item of physicalmachinery or hardware, typically mobile machinery, and which may be usedin tasks associated with agriculture. In some embodiments, a single unitof apparatus 111 may comprise a plurality of sensors 112 that arecoupled locally in a network on the apparatus; controller area network(CAN) is example of such a network that can be installed in combines,harvesters, sprayers, and cultivators. Application controller 114 iscommunicatively coupled to agricultural intelligence computer system 130via the network(s) 109 and is programmed or configured to receive one ormore scripts that are used to control an operating parameter of anagricultural vehicle or implement from the agricultural intelligencecomputer system 130. For instance, a controller area network (CAN) businterface may be used to enable communications from the agriculturalintelligence computer system 130 to the agricultural apparatus 111, suchas how the CLIMATE FIELDVIEW DRIVE, available from The ClimateCorporation, San Francisco, Calif., is used. Sensor data may consist ofthe same type of information as field data 106. In some embodiments,remote sensors 112 may not be fixed to an agricultural apparatus 111 butmay be remotely located in the field and may communicate with network109.

The apparatus 111 may comprise a cab computer 115 that is programmedwith a cab application, which may comprise a version or variant of themobile application for device 104 that is further described in othersections herein. In an embodiment, cab computer 115 comprises a compactcomputer, often a tablet-sized computer or smartphone, with a graphicalscreen display, such as a color display, that is mounted within anoperator's cab of the apparatus 111. Cab computer 115 may implement someor all of the operations and functions that are described further hereinfor the mobile computer device 104.

The network(s) 109 broadly represent any combination of one or more datacommunication networks including local area networks, wide areanetworks, internetworks or internets, using any of wireline or wirelesslinks, including terrestrial or satellite links. The network(s) may beimplemented by any medium or mechanism that provides for the exchange ofdata between the various elements of FIG. 1. The various elements ofFIG. 1 may also have direct (wired or wireless) communications links.The sensors 112, controller 114, external data server computer 108, andother elements of the system each comprise an interface compatible withthe network(s) 109 and are programmed or configured to use standardizedprotocols for communication across the networks such as TCP/IP,Bluetooth, CAN protocol and higher-layer protocols such as HTTP, TLS,and the like.

Agricultural intelligence computer system 130 is programmed orconfigured to receive field data 106 from field manager computing device104, external data 110 from external data server computer 108, andsensor data from remote sensor 112. Agricultural intelligence computersystem 130 may be further configured to host, use or execute one or morecomputer programs, other software elements, digitally programmed logicsuch as FPGAs or ASICs, or any combination thereof to performtranslation and storage of data values, construction of digital modelsof one or more crops on one or more fields, generation ofrecommendations and notifications, and generation and sending of scriptsto application controller 114, in the manner described further in othersections of this disclosure.

In an embodiment, agricultural intelligence computer system 130 isprogrammed with or comprises a communication layer 132, presentationlayer 134, data management layer 140, hardware/virtualization layer 150,and model and field data repository 160. “Layer,” in this context,refers to any combination of electronic digital interface circuits,microcontrollers, firmware such as drivers, and/or computer programs orother software elements.

Communication layer 132 may be programmed or configured to performinput/output interfacing functions including sending requests to fieldmanager computing device 104, external data server computer 108, andremote sensor 112 for field data, external data, and sensor datarespectively. Communication layer 132 may be programmed or configured tosend the received data to model and field data repository 160 to bestored as field data 106.

Presentation layer 134 may be programmed or configured to generate agraphical user interface (GUI) to be displayed on field managercomputing device 104, cab computer 115 or other computers that arecoupled to the system 130 through the network 109. The GUI may comprisecontrols for inputting data to be sent to agricultural intelligencecomputer system 130, generating requests for models and/orrecommendations, and/or displaying recommendations, notifications,models, and other field data.

Data management layer 140 may be programmed or configured to manage readoperations and write operations involving the repository 160 and otherfunctional elements of the system, including queries and result setscommunicated between the functional elements of the system and therepository. Examples of data management layer 140 include JDBC, SQLserver interface code, and/or HADOOP interface code, among others.Repository 160 may comprise a database. As used herein, the term“database” may refer to either a body of data, a relational databasemanagement system (RDBMS), or to both. As used herein, a database maycomprise any collection of data including hierarchical databases,relational databases, flat file databases, object-relational databases,object oriented databases, distributed databases, and any otherstructured collection of records or data that is stored in a computersystem. Examples of RDBMS's include, but are not limited to including,ORACLE®, MYSQL, IBM® DB2, MICROSOFT® SQL SERVER, SYBASE®, and POSTGRESQLdatabases. However, any database may be used that enables the systemsand methods described herein.

When field data 106 is not provided directly to the agriculturalintelligence computer system via one or more agricultural machines oragricultural machine devices that interacts with the agriculturalintelligence computer system, the user may be prompted via one or moreuser interfaces on the user device (served by the agriculturalintelligence computer system) to input such information. In an exampleembodiment, the user may specify identification data by accessing a mapon the user device (served by the agricultural intelligence computersystem) and selecting specific CLUs that have been graphically shown onthe map. In an alternative embodiment, the user 102 may specifyidentification data by accessing a map on the user device (served by theagricultural intelligence computer system 130) and drawing boundaries ofthe field over the map. Such CLU selection or map drawings representgeographic identifiers. In alternative embodiments, the user may specifyidentification data by accessing field identification data (provided asshape files or in a similar format) from the U. S. Department ofAgriculture Farm Service Agency or other source via the user device andproviding such field identification data to the agriculturalintelligence computer system.

In an example embodiment, the agricultural intelligence computer system130 is programmed to generate and cause displaying a graphical userinterface comprising a data manager for data input. After one or morefields have been identified using the methods described above, the datamanager may provide one or more graphical user interface widgets whichwhen selected can identify changes to the field, soil, crops, tillage,or nutrient practices. The data manager may include a timeline view, aspreadsheet view, and/or one or more editable programs.

FIG. 5 depicts an example embodiment of a timeline view for data entry.Using the display depicted in FIG. 5, a user computer can input aselection of a particular field and a particular date for the additionof event. Events depicted at the top of the timeline may includeNitrogen, Planting, Practices, and Soil. To add a nitrogen applicationevent, a user computer may provide input to select the nitrogen tab. Theuser computer may then select a location on the timeline for aparticular field in order to indicate an application of nitrogen on theselected field. In response to receiving a selection of a location onthe timeline for a particular field, the data manager may display a dataentry overlay, allowing the user computer to input data pertaining tonitrogen applications, planting procedures, soil application, tillageprocedures, irrigation practices, or other information relating to theparticular field. For example, if a user computer selects a portion ofthe timeline and indicates an application of nitrogen, then the dataentry overlay may include fields for inputting an amount of nitrogenapplied, a date of application, a type of fertilizer used, and any otherinformation related to the application of nitrogen.

In an embodiment, the data manager provides an interface for creatingone or more programs. “Program,” in this context, refers to a set ofdata pertaining to nitrogen applications, planting procedures, soilapplication, tillage procedures, irrigation practices, or otherinformation that may be related to one or more fields, and that can bestored in digital data storage for reuse as a set in other operations.After a program has been created, it may be conceptually applied to oneor more fields and references to the program may be stored in digitalstorage in association with data identifying the fields. Thus, insteadof manually entering identical data relating to the same nitrogenapplications for multiple different fields, a user computer may create aprogram that indicates a particular application of nitrogen and thenapply the program to multiple different fields. For example, in thetimeline view of FIG. 5, the top two timelines have the “Spring applied”program selected, which includes an application of 150 lbs N/ac in earlyApril. The data manager may provide an interface for editing a program.In an embodiment, when a particular program is edited, each field thathas selected the particular program is edited. For example, in FIG. 5,if the “Spring applied” program is edited to reduce the application ofnitrogen to 130 lbs N/ac, the top two fields may be updated with areduced application of nitrogen based on the edited program.

In an embodiment, in response to receiving edits to a field that has aprogram selected, the data manager removes the correspondence of thefield to the selected program. For example, if a nitrogen application isadded to the top field in FIG. 5, the interface may update to indicatethat the “Spring applied” program is no longer being applied to the topfield. While the nitrogen application in early April may remain, updatesto the “Spring applied” program would not alter the April application ofnitrogen.

FIG. 6 depicts an example embodiment of a spreadsheet view for dataentry. Using the display depicted in FIG. 6, a user can create and editinformation for one or more fields. The data manager may includespreadsheets for inputting information with respect to Nitrogen,Planting, Practices, and Soil as depicted in FIG. 6. To edit aparticular entry, a user computer may select the particular entry in thespreadsheet and update the values. For example, FIG. 6 depicts anin-progress update to a target yield value for the second field.Additionally, a user computer may select one or more fields in order toapply one or more programs. In response to receiving a selection of aprogram for a particular field, the data manager may automaticallycomplete the entries for the particular field based on the selectedprogram. As with the timeline view, the data manager may update theentries for each field associated with a particular program in responseto receiving an update to the program. Additionally, the data managermay remove the correspondence of the selected program to the field inresponse to receiving an edit to one of the entries for the field.

In an embodiment, model and field data is stored in model and field datarepository 160. Model data comprises data models created for one or morefields. For example, a crop model may include a digitally constructedmodel of the development of a crop on the one or more fields. “Model,”in this context, refers to an electronic digitally stored set ofexecutable instructions and data values, associated with one another,which are capable of receiving and responding to a programmatic or otherdigital call, invocation, or request for resolution based upon specifiedinput values, to yield one or more stored or calculated output valuesthat can serve as the basis of computer-implemented recommendations,output data displays, or machine control, among other things. Persons ofskill in the field find it convenient to express models usingmathematical equations, but that form of expression does not confine themodels disclosed herein to abstract concepts; instead, each model hereinhas a practical application in a computer in the form of storedexecutable instructions and data that implement the model using thecomputer. The model may include a model of past events on the one ormore fields, a model of the current status of the one or more fields,and/or a model of predicted events on the one or more fields. Model andfield data may be stored in data structures in memory, rows in adatabase table, in flat files or spreadsheets, or other forms of storeddigital data.

In an embodiment, agricultural intelligence computer system 130 isprogrammed to comprise a classification model management server computer(server) 170. The server 170 is further configured to comprise modelconstruction instructions 174, model execution instructions 176, anduser interface instructions 178.

In some embodiments, the model construction instructions 174 offercomputer-executable instructions to assemble training sets and builddigital models from the training sets for recognizing plant diseaseshaving multi-sized symptoms from a plant image. Each digital model isdesigned to recognize plant diseases having similar-sized symptoms.Therefore, each training set includes images corresponding to a distinctfield of view or a distinctly sized area within a plant leaf. The modelconfiguration instructions 172 offer computer-executable instructions tospecifically split given images with a sliding window into individualregions for the training sets. Each digital model can be implementedwith a deep learning architecture that classifies a new image into oneof a plurality of classes, each corresponding to a plant disease, ahealthy condition, or a catch-call combination of multiple plantdiseases.

In some embodiments, the model execution instructions 176 offercomputer-executable instructions to apply the digital models to newimages for classification. Each new image can be a new plant photoshowing multi-sized symptoms of one or more plant diseases. A firstdigital model for recognizing a first group of diseases having symptomssized within a first distinct range is applied to the new image. Morespecifically, the new image can be scaled as necessary and differentfirst regions of the new image can be classified with the first digitalmodel into a class corresponding to one of the first group of plantdiseases, a healthy condition, or a catch-all class for all other plantdiseases. The size of each first region would correlate with the sizesin the first distinct range. Next, a second digital model forrecognizing a second group of diseases having symptoms sized within asecond distinct range is applied to the combination of first regionsclassified into the catch-all class. The remaining process related tothe first digital model can similarly be performed with the seconddigital model or additional digital models until every area of the newimage is classified into at least one class corresponding to one of theplant diseases.

In some embodiments, the user interface instructions 178 offercomputer-executable instructions to manage communications with otherdevices. The communications may include receiving initial image data fortraining purposes from an image source, receiving a new photo forclassification from a user device, sending classification results forthe new photo to the user device, or sending the digital models toanother computing device.

Each component of the server 170 comprises a set of one or more pages ofmain memory, such as RAM, in the agricultural intelligence computersystem 130 into which executable instructions have been loaded and whichwhen executed cause the agricultural intelligence computing system toperform the functions or operations that are described herein withreference to those modules. For example, the model construction module174 may comprise a set of pages in RAM that contain instructions whichwhen executed cause performing the location selection functions that aredescribed herein. The instructions may be in machine executable code inthe instruction set of a CPU and may have been compiled based uponsource code written in JAVA, C, C++, OBJECTIVE-C, or any otherhuman-readable programming language or environment, alone or incombination with scripts in JAVASCRIPT, other scripting languages andother programming source text. The term “pages” is intended to referbroadly to any region within main memory and the specific terminologyused in a system may vary depending on the memory architecture orprocessor architecture. In another embodiment, each component of theserver 170 also may represent one or more files or projects of sourcecode that are digitally stored in a mass storage device such asnon-volatile RAM or disk storage, in the agricultural intelligencecomputer system 130 or a separate repository system, which when compiledor interpreted cause generating executable instructions which whenexecuted cause the agricultural intelligence computing system to performthe functions or operations that are described herein with reference tothose modules. In other words, the drawing figure may represent themanner in which programmers or software developers organize and arrangesource code for later compilation into an executable, or interpretationinto bytecode or the equivalent, for execution by the agriculturalintelligence computer system 130.

Hardware/virtualization layer 150 comprises one or more centralprocessing units (CPUs), memory controllers, and other devices,components, or elements of a computer system such as volatile ornon-volatile memory, non-volatile storage such as disk, and I/O devicesor interfaces as illustrated and described, for example, in connectionwith FIG. 4. The layer 150 also may comprise programmed instructionsthat are configured to support virtualization, containerization, orother technologies.

For purposes of illustrating a clear example, FIG. 1 shows a limitednumber of instances of certain functional elements. However, in otherembodiments, there may be any number of such elements. For example,embodiments may use thousands or millions of different mobile computingdevices 104 associated with different users. Further, the system 130and/or external data server computer 108 may be implemented using two ormore processors, cores, clusters, or instances of physical machines orvirtual machines, configured in a discrete location or co-located withother elements in a datacenter, shared computing facility or cloudcomputing facility.

2.2. Application Program Overview

In an embodiment, the implementation of the functions described hereinusing one or more computer programs or other software elements that areloaded into and executed using one or more general-purpose computerswill cause the general-purpose computers to be configured as aparticular machine or as a computer that is specially adapted to performthe functions described herein. Further, each of the flow diagrams thatare described further herein may serve, alone or in combination with thedescriptions of processes and functions in prose herein, as algorithms,plans or directions that may be used to program a computer or logic toimplement the functions that are described. In other words, all theprose text herein, and all the drawing figures, together are intended toprovide disclosure of algorithms, plans or directions that aresufficient to permit a skilled person to program a computer to performthe functions that are described herein, in combination with the skilland knowledge of such a person given the level of skill that isappropriate for inventions and disclosures of this type.

In an embodiment, user 102 interacts with agricultural intelligencecomputer system 130 using field manager computing device 104 configuredwith an operating system and one or more application programs or apps;the field manager computing device 104 also may interoperate with theagricultural intelligence computer system independently andautomatically under program control or logical control and direct userinteraction is not always required. Field manager computing device 104broadly represents one or more of a smart phone, PDA, tablet computingdevice, laptop computer, desktop computer, workstation, or any othercomputing device capable of transmitting and receiving information andperforming the functions described herein. Field manager computingdevice 104 may communicate via a network using a mobile applicationstored on field manager computing device 104, and in some embodiments,the device may be coupled using a cable 113 or connector to the sensor112 and/or controller 114. A particular user 102 may own, operate orpossess and use, in connection with system 130, more than one fieldmanager computing device 104 at a time.

The mobile application may provide client-side functionality, via thenetwork to one or more mobile computing devices. In an exampleembodiment, field manager computing device 104 may access the mobileapplication via a web browser or a local client application or app.Field manager computing device 104 may transmit data to, and receivedata from, one or more front-end servers, using web-based protocols orformats such as HTTP, XML, and/or JSON, or app-specific protocols. In anexample embodiment, the data may take the form of requests and userinformation input, such as field data, into the mobile computing device.In some embodiments, the mobile application interacts with locationtracking hardware and software on field manager computing device 104which determines the location of field manager computing device 104using standard tracking techniques such as multilateration of radiosignals, the global positioning system (GPS), WiFi positioning systems,or other methods of mobile positioning. In some cases, location data orother data associated with the device 104, user 102, and/or useraccount(s) may be obtained by queries to an operating system of thedevice or by requesting an app on the device to obtain data from theoperating system.

In an embodiment, field manager computing device 104 sends field data106 to agricultural intelligence computer system 130 comprising orincluding, but not limited to, data values representing one or more of:a geographical location of the one or more fields, tillage informationfor the one or more fields, crops planted in the one or more fields, andsoil data extracted from the one or more fields. Field manager computingdevice 104 may send field data 106 in response to user input from user102 specifying the data values for the one or more fields. Additionally,field manager computing device 104 may automatically send field data 106when one or more of the data values becomes available to field managercomputing device 104. For example, field manager computing device 104may be communicatively coupled to remote sensor 112 and/or applicationcontroller 114 which include an irrigation sensor and/or irrigationcontroller. In response to receiving data indicating that applicationcontroller 114 released water onto the one or more fields, field managercomputing device 104 may send field data 106 to agriculturalintelligence computer system 130 indicating that water was released onthe one or more fields. Field data 106 identified in this disclosure maybe input and communicated using electronic digital data that iscommunicated between computing devices using parameterized URLs overHTTP, or another suitable communication or messaging protocol.

A commercial example of the mobile application is CLIMATE FIELDVIEW,commercially available from The Climate Corporation, San Francisco,Calif. The CLIMATE FIELDVIEW application, or other applications, may bemodified, extended, or adapted to include features, functions, andprogramming that have not been disclosed earlier than the filing date ofthis disclosure. In one embodiment, the mobile application comprises anintegrated software platform that allows a grower to make fact-baseddecisions for their operation because it combines historical data aboutthe grower's fields with any other data that the grower wishes tocompare. The combinations and comparisons may be performed in real timeand are based upon scientific models that provide potential scenarios topermit the grower to make better, more informed decisions.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution. In FIG. 2, each named element represents a regionof one or more pages of RAM or other main memory, or one or more blocksof disk storage or other non-volatile storage, and the programmedinstructions within those regions. In one embodiment, in view (a), amobile computer application 200 comprises account-fields-dataingestion-sharing instructions 202, overview and alert instructions 204,digital map book instructions 206, seeds and planting instructions 208,nitrogen instructions 210, weather instructions 212, field healthinstructions 214, and performance instructions 216.

In one embodiment, a mobile computer application 200 comprises account,fields, data ingestion, sharing instructions 202 which are programmed toreceive, translate, and ingest field data from third party systems viamanual upload or APIs. Data types may include field boundaries, yieldmaps, as-planted maps, soil test results, as-applied maps, and/ormanagement zones, among others. Data formats may include shape files,native data formats of third parties, and/or farm management informationsystem (FMIS) exports, among others. Receiving data may occur via manualupload, e-mail with attachment, external APIs that push data to themobile application, or instructions that call APIs of external systemsto pull data into the mobile application. In one embodiment, mobilecomputer application 200 comprises a data inbox. In response toreceiving a selection of the data inbox, the mobile computer application200 may display a graphical user interface for manually uploading datafiles and importing uploaded files to a data manager.

In one embodiment, digital map book instructions 206 comprise field mapdata layers stored in device memory and are programmed with datavisualization tools and geospatial field notes. This provides growerswith convenient information close at hand for reference, logging andvisual insights into field performance. In one embodiment, overview andalert instructions 204 are programmed to provide an operation-wide viewof what is important to the grower, and timely recommendations to takeaction or focus on particular issues. This permits the grower to focustime on what needs attention, to save time and preserve yield throughoutthe season. In one embodiment, seeds and planting instructions 208 areprogrammed to provide tools for seed selection, hybrid placement, andscript creation, including variable rate (VR) script creation, basedupon scientific models and empirical data. This enables growers tomaximize yield or return on investment through optimized seed purchase,placement and population.

In one embodiment, script generation instructions 205 are programmed toprovide an interface for generating scripts, including variable rate(VR) fertility scripts. The interface enables growers to create scriptsfor field implements, such as nutrient applications, planting, andirrigation. For example, a planting script interface may comprise toolsfor identifying a type of seed for planting. Upon receiving a selectionof the seed type, mobile computer application 200 may display one ormore fields broken into management zones, such as the field map datalayers created as part of digital map book instructions 206. In oneembodiment, the management zones comprise soil zones along with a panelidentifying each soil zone and a soil name, texture, drainage for eachzone, or other field data. Mobile computer application 200 may alsodisplay tools for editing or creating such, such as graphical tools fordrawing management zones, such as soil zones, over a map of one or morefields. Planting procedures may be applied to all management zones ordifferent planting procedures may be applied to different subsets ofmanagement zones. When a script is created, mobile computer application200 may make the script available for download in a format readable byan application controller, such as an archived or compressed format.Additionally, and/or alternatively, a script may be sent directly to cabcomputer 115 from mobile computer application 200 and/or uploaded to oneor more data servers and stored for further use.

In one embodiment, nitrogen instructions 210 are programmed to providetools to inform nitrogen decisions by visualizing the availability ofnitrogen to crops. This enables growers to maximize yield or return oninvestment through optimized nitrogen application during the season.Example programmed functions include displaying images such as SSURGOimages to enable drawing of fertilizer application zones and/or imagesgenerated from subfield soil data, such as data obtained from sensors,at a high spatial resolution (as fine as millimeters or smallerdepending on sensor proximity and resolution); upload of existinggrower-defined zones; providing a graph of plant nutrient availabilityand/or a map to enable tuning application(s) of nitrogen across multiplezones; output of scripts to drive machinery; tools for mass data entryand adjustment; and/or maps for data visualization, among others. “Massdata entry,” in this context, may mean entering data once and thenapplying the same data to multiple fields and/or zones that have beendefined in the system; example data may include nitrogen applicationdata that is the same for many fields and/or zones of the same grower,but such mass data entry applies to the entry of any type of field datainto the mobile computer application 200. For example, nitrogeninstructions 210 may be programmed to accept definitions of nitrogenapplication and practices programs and to accept user input specifyingto apply those programs across multiple fields. “Nitrogen applicationprograms,” in this context, refers to stored, named sets of data thatassociates: a name, color code or other identifier, one or more dates ofapplication, types of material or product for each of the dates andamounts, method of application or incorporation such as injected orbroadcast, and/or amounts or rates of application for each of the dates,crop or hybrid that is the subject of the application, among others.“Nitrogen practices programs,” in this context, refer to stored, namedsets of data that associates: a practices name; a previous crop; atillage system; a date of primarily tillage; one or more previoustillage systems that were used; one or more indicators of applicationtype, such as manure, that were used. Nitrogen instructions 210 also maybe programmed to generate and cause displaying a nitrogen graph, whichindicates projections of plant use of the specified nitrogen and whethera surplus or shortfall is predicted; in some embodiments, differentcolor indicators may signal a magnitude of surplus or magnitude ofshortfall. In one embodiment, a nitrogen graph comprises a graphicaldisplay in a computer display device comprising a plurality of rows,each row associated with and identifying a field; data specifying whatcrop is planted in the field, the field size, the field location, and agraphic representation of the field perimeter; in each row, a timelineby month with graphic indicators specifying each nitrogen applicationand amount at points correlated to month names; and numeric and/orcolored indicators of surplus or shortfall, in which color indicatesmagnitude.

In one embodiment, the nitrogen graph may include one or more user inputfeatures, such as dials or slider bars, to dynamically change thenitrogen planting and practices programs so that a user may optimize hisnitrogen graph. The user may then use his optimized nitrogen graph andthe related nitrogen planting and practices programs to implement one ormore scripts, including variable rate (VR) fertility scripts. Nitrogeninstructions 210 also may be programmed to generate and cause displayinga nitrogen map, which indicates projections of plant use of thespecified nitrogen and whether a surplus or shortfall is predicted; insome embodiments, different color indicators may signal a magnitude ofsurplus or magnitude of shortfall. The nitrogen map may displayprojections of plant use of the specified nitrogen and whether a surplusor shortfall is predicted for different times in the past and the future(such as daily, weekly, monthly or yearly) using numeric and/or coloredindicators of surplus or shortfall, in which color indicates magnitude.In one embodiment, the nitrogen map may include one or more user inputfeatures, such as dials or slider bars, to dynamically change thenitrogen planting and practices programs so that a user may optimize hisnitrogen map, such as to obtain a preferred amount of surplus toshortfall. The user may then use his optimized nitrogen map and therelated nitrogen planting and practices programs to implement one ormore scripts, including variable rate (VR) fertility scripts. In otherembodiments, similar instructions to the nitrogen instructions 210 couldbe used for application of other nutrients (such as phosphorus andpotassium), application of pesticide, and irrigation programs.

In one embodiment, weather instructions 212 are programmed to providefield-specific recent weather data and forecasted weather information.This enables growers to save time and have an efficient integrateddisplay with respect to daily operational decisions.

In one embodiment, field health instructions 214 are programmed toprovide timely remote sensing images highlighting in-season cropvariation and potential concerns. Example programmed functions includecloud checking, to identify possible clouds or cloud shadows;determining nitrogen indices based on field images; graphicalvisualization of scouting layers, including, for example, those relatedto field health, and viewing and/or sharing of scouting notes; and/ordownloading satellite images from multiple sources and prioritizing theimages for the grower, among others.

In one embodiment, performance instructions 216 are programmed toprovide reports, analysis, and insight tools using on-farm data forevaluation, insights and decisions. This enables the grower to seekimproved outcomes for the next year through fact-based conclusions aboutwhy return on investment was at prior levels, and insight intoyield-limiting factors. The performance instructions 216 may beprogrammed to communicate via the network(s) 109 to back-end analyticsprograms executed at agricultural intelligence computer system 130and/or external data server computer 108 and configured to analyzemetrics such as yield, yield differential, hybrid, population, SSURGOzone, soil test properties, or elevation, among others. Programmedreports and analysis may include yield variability analysis, treatmenteffect estimation, benchmarking of yield and other metrics against othergrowers based on anonymized data collected from many growers, or datafor seeds and planting, among others.

Applications having instructions configured in this way may beimplemented for different computing device platforms while retaining thesame general user interface appearance. For example, the mobileapplication may be programmed for execution on tablets, smartphones, orserver computers that are accessed using browsers at client computers.Further, the mobile application as configured for tablet computers orsmartphones may provide a full app experience or a cab app experiencethat is suitable for the display and processing capabilities of cabcomputer 115. For example, referring now to view (b) of FIG. 2, in oneembodiment a cab computer application 220 may comprise maps-cabinstructions 222, remote view instructions 224, data collect andtransfer instructions 226, machine alerts instructions 228, scripttransfer instructions 230, and scouting-cab instructions 232. The codebase for the instructions of view (b) may be the same as for view (a)and executables implementing the code may be programmed to detect thetype of platform on which they are executing and to expose, through agraphical user interface, only those functions that are appropriate to acab platform or full platform. This approach enables the system torecognize the distinctly different user experience that is appropriatefor an in-cab environment and the different technology environment ofthe cab. The maps-cab instructions 222 may be programmed to provide mapviews of fields, farms or regions that are useful in directing machineoperation. The remote view instructions 224 may be programmed to turnon, manage, and provide views of machine activity in real-time or nearreal-time to other computing devices connected to the system 130 viawireless networks, wired connectors or adapters, and the like. The datacollect and transfer instructions 226 may be programmed to turn on,manage, and provide transfer of data collected at sensors andcontrollers to the system 130 via wireless networks, wired connectors oradapters, and the like. The machine alerts instructions 228 may beprogrammed to detect issues with operations of the machine or tools thatare associated with the cab and generate operator alerts. The scripttransfer instructions 230 may be configured to transfer in scripts ofinstructions that are configured to direct machine operations or thecollection of data. The scouting-cab instructions 232 may be programmedto display location-based alerts and information received from thesystem 130 based on the location of the field manager computing device104, agricultural apparatus 111, or sensors 112 in the field and ingest,manage, and provide transfer of location-based scouting observations tothe system 130 based on the location of the agricultural apparatus 111or sensors 112 in the field.

2.3. Data Ingest to the Computer System

In an embodiment, external data server computer 108 stores external data110, including soil data representing soil composition for the one ormore fields and weather data representing temperature and precipitationon the one or more fields. The weather data may include past and presentweather data as well as forecasts for future weather data. In anembodiment, external data server computer 108 comprises a plurality ofservers hosted by different entities. For example, a first server maycontain soil composition data while a second server may include weatherdata. Additionally, soil composition data may be stored in multipleservers. For example, one server may store data representing percentageof sand, silt, and clay in the soil while a second server may store datarepresenting percentage of organic matter (OM) in the soil.

In an embodiment, remote sensor 112 comprises one or more sensors thatare programmed or configured to produce one or more observations. Remotesensor 112 may be aerial sensors, such as satellites, vehicle sensors,planting equipment sensors, tillage sensors, fertilizer or insecticideapplication sensors, harvester sensors, and any other implement capableof receiving data from the one or more fields. In an embodiment,application controller 114 is programmed or configured to receiveinstructions from agricultural intelligence computer system 130.Application controller 114 may also be programmed or configured tocontrol an operating parameter of an agricultural vehicle or implement.For example, an application controller may be programmed or configuredto control an operating parameter of a vehicle, such as a tractor,planting equipment, tillage equipment, fertilizer or insecticideequipment, harvester equipment, or other farm implements such as a watervalve. Other embodiments may use any combination of sensors andcontrollers, of which the following are merely selected examples.

The system 130 may obtain or ingest data under user 102 control, on amass basis from a large number of growers who have contributed data to ashared database system. This form of obtaining data may be termed“manual data ingest” as one or more user-controlled computer operationsare requested or triggered to obtain data for use by the system 130. Asan example, the CLIMATE FIELDVIEW application, commercially availablefrom The Climate Corporation, San Francisco, Calif., may be operated toexport data to system 130 for storing in the repository 160.

For example, seed monitor systems can both control planter apparatuscomponents and obtain planting data, including signals from seed sensorsvia a signal harness that comprises a CAN backbone and point-to-pointconnections for registration and/or diagnostics. Seed monitor systemscan be programmed or configured to display seed spacing, population andother information to the user via the cab computer 115 or other deviceswithin the system 130. Examples are disclosed in U.S. Pat. No. 8,738,243and US Pat. Pub. 20150094916, and the present disclosure assumesknowledge of those other patent disclosures.

Likewise, yield monitor systems may contain yield sensors for harvesterapparatus that send yield measurement data to the cab computer 115 orother devices within the system 130. Yield monitor systems may utilizeone or more remote sensors 112 to obtain grain moisture measurements ina combine or other harvester and transmit these measurements to the uservia the cab computer 115 or other devices within the system 130.

In an embodiment, examples of sensors 112 that may be used with anymoving vehicle or apparatus of the type described elsewhere hereininclude kinematic sensors and position sensors. Kinematic sensors maycomprise any of speed sensors such as radar or wheel speed sensors,accelerometers, or gyros. Position sensors may comprise GPS receivers ortransceivers, or WiFi-based position or mapping apps that are programmedto determine location based upon nearby WiFi hotspots, among others.

In an embodiment, examples of sensors 112 that may be used with tractorsor other moving vehicles include engine speed sensors, fuel consumptionsensors, area counters or distance counters that interact with GPS orradar signals, PTO (power take-off) speed sensors, tractor hydraulicssensors configured to detect hydraulics parameters such as pressure orflow, and/or and hydraulic pump speed, wheel speed sensors or wheelslippage sensors. In an embodiment, examples of controllers 114 that maybe used with tractors include hydraulic directional controllers,pressure controllers, and/or flow controllers; hydraulic pump speedcontrollers; speed controllers or governors; hitch position controllers;or wheel position controllers provide automatic steering.

In an embodiment, examples of sensors 112 that may be used with seedplanting equipment such as planters, drills, or air seeders include seedsensors, which may be optical, electromagnetic, or impact sensors;downforce sensors such as load pins, load cells, pressure sensors; soilproperty sensors such as reflectivity sensors, moisture sensors,electrical conductivity sensors, optical residue sensors, or temperaturesensors; component operating criteria sensors such as planting depthsensors, downforce cylinder pressure sensors, seed disc speed sensors,seed drive motor encoders, seed conveyor system speed sensors, or vacuumlevel sensors; or pesticide application sensors such as optical or otherelectromagnetic sensors, or impact sensors. In an embodiment, examplesof controllers 114 that may be used with such seed planting equipmentinclude: toolbar fold controllers, such as controllers for valvesassociated with hydraulic cylinders; downforce controllers, such ascontrollers for valves associated with pneumatic cylinders, airbags, orhydraulic cylinders, and programmed for applying downforce to individualrow units or an entire planter frame; planting depth controllers, suchas linear actuators; metering controllers, such as electric seed meterdrive motors, hydraulic seed meter drive motors, or swath controlclutches; hybrid selection controllers, such as seed meter drive motors,or other actuators programmed for selectively allowing or preventingseed or an air-seed mixture from delivering seed to or from seed metersor central bulk hoppers; metering controllers, such as electric seedmeter drive motors, or hydraulic seed meter drive motors; seed conveyorsystem controllers, such as controllers for a belt seed deliveryconveyor motor; marker controllers, such as a controller for a pneumaticor hydraulic actuator; or pesticide application rate controllers, suchas metering drive controllers, orifice size or position controllers.

In an embodiment, examples of sensors 112 that may be used with tillageequipment include position sensors for tools such as shanks or discs;tool position sensors for such tools that are configured to detectdepth, gang angle, or lateral spacing; downforce sensors; or draft forcesensors. In an embodiment, examples of controllers 114 that may be usedwith tillage equipment include downforce controllers or tool positioncontrollers, such as controllers configured to control tool depth, gangangle, or lateral spacing.

In an embodiment, examples of sensors 112 that may be used in relationto apparatus for applying fertilizer, insecticide, fungicide and thelike, such as on-planter starter fertilizer systems, subsoil fertilizerapplicators, or fertilizer sprayers, include: fluid system criteriasensors, such as flow sensors or pressure sensors; sensors indicatingwhich spray head valves or fluid line valves are open; sensorsassociated with tanks, such as fill level sensors; sectional orsystem-wide supply line sensors, or row-specific supply line sensors; orkinematic sensors such as accelerometers disposed on sprayer booms. Inan embodiment, examples of controllers 114 that may be used with suchapparatus include pump speed controllers; valve controllers that areprogrammed to control pressure, flow, direction, PWM and the like; orposition actuators, such as for boom height, subsoiler depth, or boomposition.

In an embodiment, examples of sensors 112 that may be used withharvesters include yield monitors, such as impact plate strain gauges orposition sensors, capacitive flow sensors, load sensors, weight sensors,or torque sensors associated with elevators or augers, or optical orother electromagnetic grain height sensors; grain moisture sensors, suchas capacitive sensors; grain loss sensors, including impact, optical, orcapacitive sensors; header operating criteria sensors such as headerheight, header type, deck plate gap, feeder speed, and reel speedsensors; separator operating criteria sensors, such as concaveclearance, rotor speed, shoe clearance, or chaffer clearance sensors;auger sensors for position, operation, or speed; or engine speedsensors. In an embodiment, examples of controllers 114 that may be usedwith harvesters include header operating criteria controllers forelements such as header height, header type, deck plate gap, feederspeed, or reel speed; separator operating criteria controllers forfeatures such as concave clearance, rotor speed, shoe clearance, orchaffer clearance; or controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 that may be used with graincarts include weight sensors, or sensors for auger position, operation,or speed. In an embodiment, examples of controllers 114 that may be usedwith grain carts include controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 and controllers 114 may beinstalled in unmanned aerial vehicle (UAV) apparatus or “drones.” Suchsensors may include cameras with detectors effective for any range ofthe electromagnetic spectrum including visible light, infrared,ultraviolet, near-infrared (NIR), and the like; accelerometers;altimeters; temperature sensors; humidity sensors; pitot tube sensors orother airspeed or wind velocity sensors; battery life sensors; or radaremitters and reflected radar energy detection apparatus; otherelectromagnetic radiation emitters and reflected electromagneticradiation detection apparatus. Such controllers may include guidance ormotor control apparatus, control surface controllers, cameracontrollers, or controllers programmed to turn on, operate, obtain datafrom, manage and configure any of the foregoing sensors. Examples aredisclosed in U.S. patent application Ser. No. 14/831,165 and the presentdisclosure assumes knowledge of that other patent disclosure.

In an embodiment, sensors 112 and controllers 114 may be affixed to soilsampling and measurement apparatus that is configured or programmed tosample soil and perform soil chemistry tests, soil moisture tests, andother tests pertaining to soil. For example, the apparatus disclosed inU.S. Pat. Nos. 8,767,194 and 8,712,148 may be used, and the presentdisclosure assumes knowledge of those patent disclosures.

In an embodiment, sensors 112 and controllers 114 may comprise weatherdevices for monitoring weather conditions of fields. For example, theapparatus disclosed in U.S. patent application Ser. No. 15/551,582,filed on Aug. 16, 2017, may be used, and the present disclosure assumesknowledge of those patent disclosures.

2.4. Process Overview—Agronomic Model Training

In an embodiment, the agricultural intelligence computer system 130 isprogrammed or configured to create an agronomic model. In this context,an agronomic model is a data structure in memory of the agriculturalintelligence computer system 130 that comprises field data 106, such asidentification data and harvest data for one or more fields. Theagronomic model may also comprise calculated agronomic properties whichdescribe either conditions which may affect the growth of one or morecrops on a field, or properties of the one or more crops, or both.Additionally, an agronomic model may comprise recommendations based onagronomic factors such as crop recommendations, irrigationrecommendations, planting recommendations, fertilizer recommendations,fungicide recommendations, pesticide recommendations, harvestingrecommendations and other crop management recommendations. The agronomicfactors may also be used to estimate one or more crop related results,such as agronomic yield. The agronomic yield of a crop is an estimate ofquantity of the crop that is produced, or in some examples the revenueor profit obtained from the produced crop.

In an embodiment, the agricultural intelligence computer system 130 mayuse a preconfigured agronomic model to calculate agronomic propertiesrelated to currently received location and crop information for one ormore fields. The preconfigured agronomic model is based upon previouslyprocessed field data, including but not limited to, identification data,harvest data, fertilizer data, and weather data. The preconfiguredagronomic model may have been cross validated to ensure accuracy of themodel. Cross validation may include comparison to ground truthing thatcompares predicted results with actual results on a field, such as acomparison of precipitation estimate with a rain gauge or sensorproviding weather data at the same or nearby location or an estimate ofnitrogen content with a soil sample measurement.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using field data provided by one or more data sources.FIG. 3 may serve as an algorithm or instructions for programming thefunctional elements of the agricultural intelligence computer system 130to perform the operations that are now described.

At block 305, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic data preprocessing offield data received from one or more data sources. The field datareceived from one or more data sources may be preprocessed for thepurpose of removing noise, distorting effects, and confounding factorswithin the agronomic data including measured outliers that couldadversely affect received field data values. Embodiments of agronomicdata preprocessing may include, but are not limited to, removing datavalues commonly associated with outlier data values, specific measureddata points that are known to unnecessarily skew other data values, datasmoothing, aggregation, or sampling techniques used to remove or reduceadditive or multiplicative effects from noise, and other filtering ordata derivation techniques used to provide clear distinctions betweenpositive and negative data inputs.

At block 310, the agricultural intelligence computer system 130 isconfigured or programmed to perform data subset selection using thepreprocessed field data in order to identify datasets useful for initialagronomic model generation. The agricultural intelligence computersystem 130 may implement data subset selection techniques including, butnot limited to, a genetic algorithm method, an all subset models method,a sequential search method, a stepwise regression method, a particleswarm optimization method, and an ant colony optimization method. Forexample, a genetic algorithm selection technique uses an adaptiveheuristic search algorithm, based on evolutionary principles of naturalselection and genetics, to determine and evaluate datasets within thepreprocessed agronomic data.

At block 315, the agricultural intelligence computer system 130 isconfigured or programmed to implement field dataset evaluation. In anembodiment, a specific field dataset is evaluated by creating anagronomic model and using specific quality thresholds for the createdagronomic model. Agronomic models may be compared and/or validated usingone or more comparison techniques, such as, but not limited to, rootmean square error with leave-one-out cross validation (RMSECV), meanabsolute error, and mean percentage error. For example, RMSECV can crossvalidate agronomic models by comparing predicted agronomic propertyvalues created by the agronomic model against historical agronomicproperty values collected and analyzed. In an embodiment, the agronomicdataset evaluation logic is used as a feedback loop where agronomicdatasets that do not meet configured quality thresholds are used duringfuture data subset selection steps (block 310).

At block 320, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic model creation basedupon the cross validated agronomic datasets. In an embodiment, agronomicmodel creation may implement multivariate regression techniques tocreate preconfigured agronomic data models.

At block 325, the agricultural intelligence computer system 130 isconfigured or programmed to store the preconfigured agronomic datamodels for future field data evaluation.

2.5. Implementation Example—Hardware Overview

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs) or fieldprogrammable gate arrays (FPGAs) that are persistently programmed toperform the techniques, or may include one or more general purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing devices may bedesktop computer systems, portable computer systems, handheld devices,networking devices or any other device that incorporates hard-wiredand/or program logic to implement the techniques.

For example, FIG. 4 is a block diagram that illustrates a computersystem 400 upon which an embodiment of the invention may be implemented.Computer system 400 includes a bus 402 or other communication mechanismfor communicating information, and a hardware processor 404 coupled withbus 402 for processing information. Hardware processor 404 may be, forexample, a general purpose microprocessor.

Computer system 400 also includes a main memory 406, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 402for storing information and instructions to be executed by processor404. Main memory 406 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 404. Such instructions, when stored innon-transitory storage media accessible to processor 404, rendercomputer system 400 into a special-purpose machine that is customized toperform the operations specified in the instructions.

Computer system 400 further includes a read only memory (ROM) 408 orother static storage device coupled to bus 402 for storing staticinformation and instructions for processor 404. A storage device 410,such as a magnetic disk, optical disk, or solid-state drive is providedand coupled to bus 402 for storing information and instructions.

Computer system 400 may be coupled via bus 402 to a display 412, such asa cathode ray tube (CRT), for displaying information to a computer user.An input device 414, including alphanumeric and other keys, is coupledto bus 402 for communicating information and command selections toprocessor 404. Another type of user input device is cursor control 416,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 404 and forcontrolling cursor movement on display 412. This input device typicallyhas two degrees of freedom in two axes, a first axis (e.g., x) and asecond axis (e.g., y), that allows the device to specify positions in aplane.

Computer system 400 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 400 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 400 in response to processor 404 executing one or more sequencesof one or more instructions contained in main memory 406. Suchinstructions may be read into main memory 406 from another storagemedium, such as storage device 410. Execution of the sequences ofinstructions contained in main memory 406 causes processor 404 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical disks, magnetic disks, or solid-state drives, suchas storage device 410. Volatile media includes dynamic memory, such asmain memory 406. Common forms of storage media include, for example, afloppy disk, a flexible disk, hard disk, solid-state drive, magnetictape, or any other magnetic data storage medium, a CD-ROM, any otheroptical data storage medium, any physical medium with patterns of holes,a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 402. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infrared data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 404 for execution. For example,the instructions may initially be carried on a magnetic disk orsolid-state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 400 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infrared signal and appropriatecircuitry can place the data on bus 402. Bus 402 carries the data tomain memory 406, from which processor 404 retrieves and executes theinstructions. The instructions received by main memory 406 mayoptionally be stored on storage device 410 either before or afterexecution by processor 404.

Computer system 400 also includes a communication interface 418 coupledto bus 402. Communication interface 418 provides a two-way datacommunication coupling to a network link 420 that is connected to alocal network 422. For example, communication interface 418 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 418 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 418sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

Network link 420 typically provides data communication through one ormore networks to other data devices. For example, network link 420 mayprovide a connection through local network 422 to a host computer 424 orto data equipment operated by an Internet Service Provider (ISP) 426.ISP 426 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 428. Local network 422 and Internet 428 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 420and through communication interface 418, which carry the digital data toand from computer system 400, are example forms of transmission media.

Computer system 400 can send messages and receive data, includingprogram code, through the network(s), network link 420 and communicationinterface 418. In the Internet example, a server 430 might transmit arequested code for an application program through Internet 428, ISP 426,local network 422 and communication interface 418.

The received code may be executed by processor 404 as it is received,and/or stored in storage device 410, or other non-volatile storage forlater execution.

3. Functional Descriptions

3.1 Training Set and Digital Model Construction

In some embodiments, the server 170 is programmed to collect one or moretraining sets of images to train digital models for recognizing plantdiseases. For corn, the one or more training sets of images may includephotos of corn leaves. Each photo preferably shows non-overlappingdisease symptoms. The common corn diseases include Anthracnose LeafBlight (ALB), Common Rust (CR), Eyespot (EYE), Gray Leaf Spot (GLS),Goss's Wilt (GW), Northern Leaf Blight (NLB), Northern Leaf Spot (NLS),Southern Leaf Blight (SLB), and Southern Rust (SR). The symptoms ofdifferent diseases tend to look different. For example, CR, EYE, SR, andGLS at an early stage (GLS-Early) tend to produce relatively smalllesions that are dot-like or slightly elongated, while GW, NLB, and GLSat a late stage (GLS-Late) tend to produce relatively large lesions thatare strip-like or greatly elongated. Therefore, at least two trainingsets can be constructed to train at least two digital models, with eachdigital model designed to classify an input image into one or moreclasses corresponding to one or more plant diseases havingsimilarly-sized symptoms.

In some embodiments, given a specific image, the server 170 can beprogrammed to first resize the specific image to a standard size andthen extract images from the resized image for a training set using asliding window with a certain stride (the number of pixels to shift thesliding window over the input image). The server 170 can be programmedto further assign a class label of one of the one or more classes notedabove to each of the extracted images. Specifically, the server 170 canbe programmed to receive the class label from an expert or automaticallydetermine the class label based on images of known disease symptoms. Forexample, an image of a symptom of a known disease at an appropriateresolution can be matched to an extracted image using any matchingtechnique known to someone skilled in the art, and the extracted imagecan be assigned a class label corresponding to the known disease whenthe match is successful.

FIG. 7A illustrates an example approach of extracting sample images froma photo showing symptoms of a plant disease that are relatively small.In some embodiments, the given image is a photo of a corn leaf havingsymptoms of SR. The server 170 can be programmed to resize the givenimage into the image 702 to a first size with a first scaling factorrelatively to a fixed-sized sliding window, such as from 3,000 pixels by4,000 pixels to 1,120 (224*5) pixels by 1,493 pixels with the firstscaling factor being 5 relative to a sliding window having a size of 224pixels by 224 pixels, using a resizing technique known to someoneskilled in the art. The image 720 still shows relatively small symptomsof SR, such as the lesion in the area 710. The server 170 is programmedto then apply a sliding window that is relatively small to the image 702row by row or column by column with a certain stride that determineswhere the next position of the sliding window is relative to the currentposition. For example, with the sliding window having a size of 224pixels by 224 pixels, the stride can be 224 pixels leading to no overlapbetween the next position and the current position, and a total of 30images can be extracted when the image 702 has 1,120 pixels by 1,493pixels. Therefore, from an initial position 704 of the sliding window,the next position in the same row would be 706, and the next position inthe same column would be 708. The portion of the image 702 correspondingto each position of the sliding window can be extracted and assigned aclass label. In this example, the portion corresponding to the position704 can be assigned a label of “SR” for the SR disease class given thepresence of SR lesions, the portion corresponding to the position 706can similarly be assigned a label of “SR”, and the portion correspondingto the position 706 can be assigned a label that represents a healthycondition or a lack of disease symptoms for a no disease (ND) class.

FIG. 7B illustrates an example approach of extracting sample images froma photo showing symptoms of a plant disease that are relatively large.In some embodiments, the image can be a photo of a corn leaf havingsymptoms of GW. The server 170 is programmed to resize the given imageinto the image 712 with a second scaling factor smaller than the firstscaling factor, such as from 3,000 pixels by 4,000 pixels to 448 (224*2)pixels by 597 pixels with the second scaling factor being 2, using aresizing technique known to someone skilled in the art. The image 712still shows relatively large symptoms of GW, such as the lesion in thearea 718. The server 170 is programmed to then apply a sliding windowthat is relatively large to the image to the image 712 row by row orcolumn by column with a certain stride that determines where the nextposition of the sliding window is relative to the current position. Forexample, with the sliding window having size of 224 pixels by 224pixels, the stride can be 112 pixels leading to a half overlap betweenthe next position and the current position, and a total of 12 images canbe extracted when the image 712 has 448 pixels by 597 pixels. Therefore,from an initial position 714 of the sliding window, the next position inthe same row would be 716. The portion of the image 712 corresponding toeach position of the sliding window can be extracted and assigned aclass label. In this example, the portion corresponding to the position714 can be assigned a label of “GW” for the GW disease class given thepresence of GW lesions, and the portion corresponding to the position716 can similarly be assigned a label of “GW”.

In some embodiments, the server 170 is programmed to process a number ofimages to extract enough sample images for each of the plant diseases.The images can be retrieved from image servers or from user devices. Theimages preferably show symptoms of each plant disease in differentconditions, such as at different points within the lifecycle of theplant, resulting from different lighting conditions, or having differentshapes, sizes, or scales. To further increase the breadth of a digitalmodel, the server 170 can be programmed to include more images showingoverlapping symptoms of a plant disease having relatively large symptomsand a plant disease having relatively small symptoms to improvedetection of the relatively small symptoms. For example, these imagescan show overlapping symptoms of GLS-Late (large) and CR (small), GW(large) and CR, GW and SR (small), NLB (large) and CR, or NLB and SR.The server 170 can be programmed to further assign each image extractedfrom one of these images to the class corresponding to the dominantdisease based on the total area covered by the symptoms of each diseasein the extracted image.

In some embodiments, the server 170 is programmed to generate variantsof the extracted images to augment the training set. More specifically,the server 170 can be configured to rotate or further scale theextracted images. For corn, there can be at least 200 images for thehealthy condition and for each corn disease, including less than 10%that show overlapping symptoms. Two digital models can be constructed, afirst one for detecting corn diseases having relatively small symptomsand a second one for detecting corn diseases having relatively largesymptoms. Therefore, a first training set and a second training set canbe built respectfully for the first digital model and the second digitalmodel, as illustrated in FIG. 7A and FIG. 7B. Each training set caninclude images showing symptoms of the corn diseases to be detected bythe corresponding digital model. Depending on how the digital models areto be applied to a test image, each training set can include additionalimages. When the first digital model and the second digital model are tobe applied sequentially, as further discussed below, the first trainingset can include additional images that show symptoms of those diseaseswhich the second digital model is designed to detect and that areassigned a common label representing a catch-all class of all thosediseases. These additional images can be generated by processing (scaledto capture a certain field of view, etc.) an original image used for thesecond training set as an original image used for the first trainingset.

In some embodiments, the server 170 is programmed to build the digitalmodels for recognizing plant diseases from the training sets. Thedigital models can be any classification models known to someone skilledin the art, such as a decision tree or a CNN. For corn, the server 170can be programmed to build the two digital models from the two trainingsets, as discussed above. The first digital model is used to recognizecorn diseases having relatively small symptoms, such as CR, EYE, SR, orGLS-Early, and the second digital model is used to recognize corndiseases having relatively large symptoms, such as GW, NLB, or GLS-Late.To implement each digital model as a CNN, public libraries can be used,such as the ResNet-50 package available on the GitHub platform.

3.2 Digital Model Execution

In some embodiments, the server 170 is programmed to receive a new imageto be classified from a user device and apply the digital models to thenew image to obtain classifications. For corn, the server 170 can beprogrammed to apply the two digital models in sequence to first detectcorn diseases having relatively small symptoms and subsequently detectcorn diseases having relatively large symptoms.

FIG. 8 illustrates an example process of recognizing plant diseaseshaving multi-sized symptoms from a plant image using multiple digitalmodels. In some embodiments, the plant is corn, and the plant image is aphoto of a corn leaf. Given a new image 802 to be classified, the server170 is programmed to first apply the first digital model for recognizingcorn diseases having relatively small symptoms. Specifically, the server170 can be programmed to resize the new image 802 similarly by the firstscaling factor noted above into a resized image, such as from 3,000pixels by 4,000 pixels to 1,120 (224*5) pixels by 1,493 pixels with thefirst scaling factor being 5. The server 170 is programmed to then applya sliding window that is relatively small to the resized image row byrow or column by column with a certain stride that determines where thenext position of the sliding window is relative to the current position.The size of the sliding window would generally be equal to the size of asample image (an extracted image) used to build the first digital model.For example, the sliding window can have a size of 224 pixels by 224pixels, and the stride can be 224 pixels. For each position of thesliding window, the server 170 can be programmed to apply the firstdigital model 804 to the portion of the resized image within the slidingwindow to obtain a classification corresponding to the healthycondition, one of the corn diseases having relatively small symptoms, orthe collection of corn diseases having relatively large symptoms. Forexample, the portions 806 are classified into CR, EYE, SR, GLS-Early, orND, and the portions 812 are classified into an other diseases (OD)class.

In some embodiments, the server 170 can be programmed to map eachportion of the resized image extracted by the sliding window back into aregion of the new image 802. The server 170 is programmed to furtherprepare a prediction map for the new image 802 where each mapped regionis shown with an indicator of the corresponding classification.

FIG. 9A illustrates an example prediction map showing results ofapplying a first digital model to a plant image to recognize plantdiseases having relatively small symptoms. In some embodiments, giventhe scale between the size of the sliding window and the size of a newimage, essentially a relatively small sliding window is moved throughdifferent positions within a new image 920, including the position 902.Each region (first region) of the new image 920 corresponding to aposition of the sliding window is then labeled with the correspondingclassification in the prediction map 922 according to the legend 906.For example, the region 912 has been classified into the OD classrepresenting the combination of corn diseases having relatively largesymptoms.

Referring back to FIG. 8, in some embodiments, for the portions of theresized image that are classified into the OD class corresponding to thecollection of corn diseases having relatively large symptoms, the server170 is programmed to apply the second digital model 808 for recognizingcorn diseases having relatively large symptoms. Referring back to FIG.9A, each such portion, such as the one mapped to the region 912,corresponds to a relatively small field of view and thus typically onlypart of a relatively large symptom, as shown in the area 932. Therefore,the server 170 is programmed to apply the second digital model 808 tomultiple such portions at once. More specifically, for each suchportion, the server 170 can be configured to also consider a certainnumber of surrounding portions or a certain fraction of a surroundingportion in each direction to approximately match the field of view usedfor building the second digital model. For example, each such portion of224 pixels by 224 pixels can be considered together with one surroundingportion in each direction, leading to a combined portion of 672 (224*3)pixels by 672 pixels. The server 170 can be configured to further resizethe combined portion to the size of an input image for the seconddigital model, effecting resulting in a scaling factor of 5/3. Theserver 170 can be configured to then apply the second digital model tothe resized combined portion to obtain a classification corresponding toone of the corn diseases having relatively large symptoms. Referringback to FIG. 8, the resized combined portions 810 are classified intoGW, NLB, or GLS-Early.

In some embodiments, instead of including into the combined portion aneighboring portion that has not been classified into the ND class, theserver 170 can be programmed to mask (e.g., with zero values) each ofthe plurality of first regions in the new image 802 that is classifiedinto a class corresponding to one of the first plurality of plantdiseases or a healthy condition. In some embodiments, the server 170 canbe programmed to resize the new image 802 with masked portions similarlyby the second scaling factor noted above into a resized image, such asfrom 224 pixels by 224 pixels to 448 (224*2) pixels by 448 pixels withthe first scaling factor being 2. The server 170 can be programmed tothen apply a sliding window that is relatively large to the resizedimage row by row or column by column with a certain stride. The size ofthe sliding window would generally be equal to the size of a sampleimage (extracted image) used to train the second digital model. Forexample, the sliding window can have a size of 224 pixels by 224 pixels,and the stride can be 112 or 224 pixels. For each position of thesliding window, the server 170 can be programmed to then apply thesecond digital model 804 to the portion of the resized image or theportion corresponding to the combined portion classified into the ODclass within the sliding window to obtain a classification correspondingto one of the corn diseases having relatively large symptoms. In otherembodiments, the server 170 is programmed to include imagescorresponding to a catch-all class only in the second training set andapply the second digital model before applying the first digital modelto a new image.

Referring back to FIG. 8, in some embodiments, the server 170 can beprogrammed to similarly map each portion classified by the seconddigital model back into a region of the image 802. The server 170 isprogrammed to further update the prediction map for the image 802 whereeach newly mapped region is shown with an indicator of the correspondingclassification. The server 170 can be programmed to then transmitclassification data related to the updated prediction map to the userdevice.

FIG. 9B illustrates an example prediction map showing results ofapplying a second digital model to a plant image to recognize plantdiseases having relatively large symptoms. In some embodiments, giventhe scale between the size of the sliding window and the size of a newimage, essentially a relatively large sliding window is moved throughdifferent positions within the new image 920 or only within the portionclassified into the OD class by the first digital model, including theposition 910. Each region (second region) of the new image 920corresponding to a position of the sliding window is then labeled withthe corresponding classification in the prediction map 922, overwritingexisting values. For example, the region 912 illustrated in FIG. 9A thatwas classified into the OD class is now under the region 908 classifiedinto the class corresponding to GLS-Late. Therefore, while the new image920 shows overlapping symptoms of SR and GLS-Late, both diseases aredetected from different regions of the new image 920.

In some embodiments, the server 170 is programmed to further process theupdated prediction map. The server 170 can be configured to compute thetotal area classified into each of the classes and conclude that diseasecorresponding to the class having the largest total area is the dominantdisease in the plant captured in the new image. For example, the updatedprediction map 922 shows that symptoms of SR and GLS-Late each occupyapproximately half of the new image 920 and thus could be considered asthe dominant disease for the particular corn captured in the new image920. The server 170 can be configured to further transmit dominanceinformation related to the dominant disease to the user device.

3.3 Example Processes

FIG. 10 illustrates an example method performed by a server computerthat is programmed for recognizing plant diseases having multi-sizedsymptoms from a plant image. FIG. 10 is intended to disclose analgorithm, plan or outline that can be used to implement one or morecomputer programs or other software elements which when executed causeperforming the functional improvements and technical advances that aredescribed herein. Furthermore, the flow diagrams herein are described atthe same level of detail that persons of ordinary skill in the artordinarily use to communicate with one another about algorithms, plans,or specifications forming a basis of software programs that they plan tocode or implement using their accumulated skill and knowledge.

In some embodiments, in step 1002, the server 170 is programmed orconfigured to obtain a first training set comprising a first photoshowing a first symptom of one of a first plurality of plant diseases, asecond photo showing no symptom, and a third photo showing a partialsecond symptom of one of a second plurality of plant diseases. The firstplurality of plant diseases produce symptoms having sizes within a firstrange. The second plurality of plant diseases produce symptoms havingsizes within a second range. The first symptom is smaller than thesecond symptom, and the first, second, and third photos correspond to acommonly-sized field of view. The server 170 can be configured togenerate the first training set from photos showing multi-sized diseasesymptoms by using a sliding window suitable for capturing individualsymptoms of the first plurality of plant diseases.

In some embodiments, in step 1004, the server 170 is programmed orconfigured to build a first CNN from the first training set forclassifying an image into a class corresponding to one of the firstplurality of plant diseases, a healthy condition, or a combination ofthe second plurality of plant diseases. Therefore, when the first CNN isconfigured to recognize symptoms of k diseases, the first CNN isconfigured to classify an image into one of k+2 classes. It is alsopossible to lump the no-disease class into the catch-all class andconfigure the second CNN to classify an image into the no-disease class.

In some embodiments, in step 1006, the server 170 is programmed orconfigured to obtain a second training set comprising a photo showingthe second symptom. The server 170 can be similarly configured togenerate the second training set from photos showing multi-sized or justmultiple disease symptoms by using a sliding window suitable forcapturing individual symptoms of the second plurality of plant diseases.

In some embodiments, the server 170 is programmed or configured to builda second CNN from the second training set for classifying an image intoa class corresponding to one of the second plurality of plant diseases.The server 170 can be configured to send the first and second CNNs toanother computing device, which can then be configured to apply the twoCNNs to classify a new photo of an infected plant.

In some embodiments, in step 1008, the server 170 is programmed orconfigured to receive a new image from a user device. The new image canbe a photo of an infected plant showing multi-sized symptoms.

In some embodiments, in step 1010, the server 170 is programmed orconfigured to apply the first CNN to a plurality of first regions withinthe new image to obtain a plurality of classifications. The size of eachof the first regions is suitable for representing individual symptoms ofthe first plurality of plant diseases.

In some embodiments, in step 1012, the server 170 is programmed orconfigured to apply the second CNN to one or more second regions withincombination of first regions classified into the class corresponding tothe combination of the second plurality of plant diseases to obtain oneor more classifications, each of the plurality of first regions beingsmaller than the one second region. The size of each of the secondregions is suitable for representing individual symptoms of the secondplurality of plant diseases.

In some embodiments, in step 1014, the server 170 is programmed orconfigured to transmit classification data related to the plurality ofclassifications that are into a class corresponding to one of the firstplurality of plant diseases or the healthy condition and the one or moreclassifications to the user device. The classification data may include,for one or more regions of the new image, the size of the region and thecorresponding classification. The server 170 can be further configuredto identify a dominant disease for the new image, such as the disease towhich the largest area of the new image has been classified, and sendinformation regarding the dominant disease as part of the classificationdata.

4. Extensions and Alternatives

In the foregoing specification, embodiments of the invention have beendescribed with reference to numerous specific details that may vary fromimplementation to implementation. The specification and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense. The sole and exclusive indicator of the scope of the invention,and what is intended by the applicants to be the scope of the invention,is the literal and equivalent scope of the set of claims that issue fromthis application, in the specific form in which such claims issue,including any subsequent correction.

What is claimed is:
 1. A computer-implemented method of recognizingplant diseases from a plant image, comprising: obtaining, by aprocessor, a first digital model for classifying an image into a classof a first set of classes corresponding to a first plurality of plantdiseases, a healthy condition, or a combination of a second plurality ofplant diseases; obtaining, by the processor, a second digital model forclassifying an image into a class of a second set of classescorresponding to the second plurality of plant diseases; receiving a newimage from a user device; applying the first digital model to aplurality of first regions within the new image to obtain a plurality ofclassifications; applying the second digital model to one or more secondregions, each corresponding to a combination of multiple first regionsof the plurality of first regions, to obtain one or moreclassifications, the multiple first regions being classified into theclass corresponding to the combination of the second plurality of plantdiseases; transmitting classification data related to the plurality ofclassifications into a class corresponding to one of the first pluralityof plant diseases or the healthy condition and the one or moreclassifications to the user device.
 2. The computer-implemented methodof claim 1, where a plant disease of the first plurality of plantdiseases produces smaller lesions than a plant disease of a secondplurality of plant diseases.
 3. The computer-implemented method of claim2, the applying the first digital model comprising: resizing the newimage by a first scaling factor to obtain a resized image; applying afirst sliding window to the resized image to obtain a plurality ofportions of the resized image, a size of the first sliding window beingequal to a size of a sample image used to build the first digital model;applying the first digital model to each portion of the plurality ofportions of the resized image.
 4. The computer-implemented method ofclaim 3, further comprising: mapping each portion of the plurality ofportions of the resized image back into a region of the new image;preparing a prediction map for the new image showing an indicator of aresult of applying the first digital model to each portion of theplurality of portions of the resize image at a corresponding region ofthe new image; causing a display of the prediction map.
 5. Thecomputer-implemented method of claim 3, the applying the second digitalmodel comprising: combining multiple portions of the plurality ofportions of the resized image, at least one portion of the multipleportions not having been classified into the first set of classes, toobtain a combined portion; resizing the combined portion to a size of asample image used to build the second digital model to obtain a resizedcombined portion; applying the second digital model to the resizedcombined portion.
 6. The computer-implemented method of claim 3, theapplying the second digital model comprising: masking off regions of theplurality of first regions in the new image that were classified intothe first set of classes to obtain a modified new image; resizing themodified new image by a second scaling factor smaller than the firstscaling factor to obtain a resized modified image; applying a secondsliding window to the resized modified image to obtain a plurality ofportions of the resized modified image, a size of the second slidingwindow being equal to a size of a sample image used to build the seconddigital model; applying the second digital model to each portion of theplurality of portions of the resized modified image.
 7. Thecomputer-implemented method of claim 6, further comprising: mapping eachportion of the plurality of portions of the resized modified image backinto a region of the new image; updating a prediction map for the newimage showing an indicator of a result of applying the second digitalmodel to each portion of the plurality of portions of the resizemodified image a corresponding region of the new image; causing adisplay of the prediction map.
 8. The computer-implemented method ofclaim 3, the obtaining the first digital model comprising: obtaining afirst training set from at least a first photo showing a first symptomincluding a first lesion of one of a first plurality of plant diseases,a second photo showing no symptom, and a third photo showing a secondsymptom including a partial second lesion of one of a second pluralityof plant diseases, the first lesion being smaller than the secondlesion, the first, second, and third photos corresponding tosimilarly-sized fields of view; building the first digital model fromthe first training set.
 9. The computer-implemented method of claim 8,the obtaining the second digital model comprising: obtaining a secondtraining set from at least a fourth photo showing the second symptomincluding third lesion, the first lesion being smaller than the thirdlesion; building the second digital model from the second training set.10. The computer-implemented method of claim 8, the obtaining the firstdigital model further comprising: determining a first image size basedon the size of the first sliding window and the first scaling factor;resizing the first photo, the second photo, or the third photo accordingto the first image size to obtain a first resized photo, a second resizephoto, or a third resized photo.
 11. The computer-implemented method ofclaim 1, the first plurality of plant diseases including Common Rust,Eyespot, Southern Rust, or Gray Leaf Spot at an early stage, the secondplurality of plant diseases including Goss's Wilt, Northern Leaf Blight,or Gray Leaf Spot at a late stage.
 12. One or more non-transitorycomputer-readable storage media storing instructions which when executedcause one or more processors to perform a method of recognizing plantdiseases having multiple symptoms from a plant image, the methodcomprising: obtaining, by a processor, a first digital model forclassifying an image into a class of a first set of classescorresponding to a first plurality of plant diseases, a healthycondition, or a combination of a second plurality of plant diseases;obtaining, by the processor, a second digital model for classifying animage into a class of a second set of classes corresponding to thesecond plurality of plant diseases; receiving a new image from a userdevice; applying the first digital model to a plurality of first regionswithin the new image to obtain a plurality of classifications; applyingthe second digital model to one or more second regions, eachcorresponding to a combination of multiple first regions of theplurality of first regions, to obtain one or more classifications, themultiple first regions being classified into the class corresponding tothe combination of the second plurality of plant diseases; transmittingclassification data related to the plurality of classifications into aclass corresponding to one of the first plurality of plant diseases orthe healthy condition and the one or more classifications to the userdevice.
 13. The one or more non-transitory computer-readable storagemedia of claim 12, wherein a plant disease of the first plurality ofplant diseases produces smaller lesions than a plant disease of a secondplurality of plant diseases.
 14. The one or more non-transitorycomputer-readable storage media of claim 13, the applying the firstdigital model comprising: resizing the new image by a first scalingfactor to obtain a resized image; applying a first sliding window to theresized image to obtain a plurality of portions of the resized image, asize of the first sliding window being equal to a size of a sample imageused to build the first digital model; applying the first digital modelto each portion of the plurality of portions of the resized image. 15.The one or more non-transitory computer-readable storage media of claim14, the method further comprising: mapping each portion of the pluralityof portions of the resized image back into a region of the new image;preparing a prediction map for the new image showing an indicator of aresult of applying the first digital model to each portion of theplurality of portions of the resize image at a corresponding region ofthe new image; causing a display of the prediction map.
 16. The one ormore non-transitory computer-readable storage media of claim 14, theapplying the second digital model comprising: combining multipleportions of the plurality of portions of the resized image, at least oneportion of the multiple portions not having been classified into thefirst set of classes, to obtain a combined portion; resizing thecombined portion to a size of a sample image used to build the seconddigital model to obtain a resized combined portion; applying the seconddigital model to the resized combined portion.
 17. The one or morenon-transitory computer-readable storage media of claim 14, the applyingthe second digital model comprising: masking off regions of theplurality of first regions in the new image that were classified intothe first set of classes to obtain a modified new image; resizing themodified new image by a second scaling factor smaller than the firstscaling factor to obtain a resized modified image; applying a secondsliding window to the resized modified image to obtain a plurality ofportions of the resized modified image, a size of the second slidingwindow being equal to a size of a sample image used to build the seconddigital model; applying the second digital model to each portion of theplurality of portions of the resized modified image.
 18. The one or morenon-transitory computer-readable storage media of claim 17, the methodfurther comprising: mapping each portion of the plurality of portions ofthe resized modified image back into a region of the new image; updatinga prediction map for the new image showing an indicator of a result ofapplying the second digital model to each portion of the plurality ofportions of the resize modified image a corresponding region of the newimage; causing a display of the prediction map.
 19. The one or morenon-transitory computer-readable storage media of claim 14, theobtaining the first digital model comprising: obtaining a first trainingset from at least a first photo showing a first symptom including afirst lesion of one of a first plurality of plant diseases, a secondphoto showing no symptom, and a third photo showing a second symptomincluding a partial second lesion of one of a second plurality of plantdiseases, the first lesion being smaller than the second lesion, thefirst, second, and third photos corresponding to similarly-sized fieldsof view; building the first digital model from the first training set.20. The one or more non-transitory computer-readable storage media ofclaim 19, the obtaining the second digital model comprising: obtaining asecond training set from at least a fourth photo showing the secondsymptom including third lesion, the first lesion being smaller than thethird lesion; building the second digital model from the second trainingset.
 21. The one or more non-transitory computer-readable storage mediaof claim 19, the obtaining the first digital model further comprising:determining a first image size based on the size of the first slidingwindow and the first scaling factor; resizing the first photo, thesecond photo, or the third photo according to the first image size toobtain a first resized photo, a second resize photo, or a third resizedphoto.
 22. The one or more non-transitory computer-readable storagemedia of claim 12, the first plurality of plant diseases includingCommon Rust, Eyespot, Southern Rust, or Gray Leaf Spot at an earlystage, the second plurality of plant diseases including Goss's Wilt,Northern Leaf Blight, or Gray Leaf Spot at a late stage.